Overview

Dataset statistics

Number of variables17
Number of observations60827
Missing cells0
Missing cells (%)0.0%
Duplicate rows112
Duplicate rows (%)0.2%
Total size in memory8.4 MiB
Average record size in memory144.0 B

Variable types

Numeric16
Categorical1

Alerts

Dataset has 112 (0.2%) duplicate rowsDuplicates
AREA is highly overall correlated with PERIMETER and 3 other fieldsHigh correlation
PERIMETER is highly overall correlated with AREA and 10 other fieldsHigh correlation
MAJOR_AXIS is highly overall correlated with PERIMETER and 11 other fieldsHigh correlation
MINOR_AXIS is highly overall correlated with MAJOR_AXIS and 9 other fieldsHigh correlation
ECCENTRICITY is highly overall correlated with PERIMETER and 12 other fieldsHigh correlation
EQDIASQ is highly overall correlated with AREA and 3 other fieldsHigh correlation
SOLIDITY is highly overall correlated with MAJOR_AXIS and 6 other fieldsHigh correlation
CONVEX_AREA is highly overall correlated with AREA and 4 other fieldsHigh correlation
EXTENT is highly overall correlated with MAJOR_AXIS and 8 other fieldsHigh correlation
ASPECT_RATIO is highly overall correlated with PERIMETER and 12 other fieldsHigh correlation
ROUNDNESS is highly overall correlated with PERIMETER and 12 other fieldsHigh correlation
COMPACTNESS is highly overall correlated with PERIMETER and 12 other fieldsHigh correlation
SHAPEFACTOR_1 is highly overall correlated with MAJOR_AXIS and 9 other fieldsHigh correlation
SHAPEFACTOR_2 is highly overall correlated with PERIMETER and 11 other fieldsHigh correlation
SHAPEFACTOR_3 is highly overall correlated with PERIMETER and 12 other fieldsHigh correlation
SHAPEFACTOR_4 is highly overall correlated with ECCENTRICITY and 5 other fieldsHigh correlation
CLASS is highly overall correlated with AREA and 12 other fieldsHigh correlation

Reproduction

Analysis started2023-10-26 00:25:20.749359
Analysis finished2023-10-26 00:26:05.276365
Duration44.53 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

AREA
Real number (ℝ)

HIGH CORRELATION 

Distinct6929
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7201.6504
Minimum3929
Maximum12864
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size950.4 KiB
2023-10-26T09:26:05.375884image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3929
5-th percentile5143
Q16062
median6986
Q37957
95-th percentile10636
Maximum12864
Range8935
Interquartile range (IQR)1895

Descriptive statistics

Standard deviation1624.4442
Coefficient of variation (CV)0.22556555
Kurtosis1.8602346
Mean7201.6504
Median Absolute Deviation (MAD)948
Skewness1.2240921
Sum4.3805479 × 108
Variance2638819
MonotonicityNot monotonic
2023-10-26T09:26:05.508909image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5156 38
 
0.1%
5016 34
 
0.1%
5154 31
 
0.1%
7633 31
 
0.1%
5157 31
 
0.1%
5101 30
 
< 0.1%
5055 30
 
< 0.1%
5168 30
 
< 0.1%
6844 30
 
< 0.1%
5143 30
 
< 0.1%
Other values (6919) 60512
99.5%
ValueCountFrequency (%)
3929 1
< 0.1%
4029 1
< 0.1%
4267 1
< 0.1%
4339 1
< 0.1%
4429 1
< 0.1%
4583 1
< 0.1%
4616 1
< 0.1%
4666 1
< 0.1%
4707 1
< 0.1%
4804 1
< 0.1%
ValueCountFrequency (%)
12864 2
 
< 0.1%
12863 2
 
< 0.1%
12862 1
 
< 0.1%
12861 4
< 0.1%
12860 1
 
< 0.1%
12859 1
 
< 0.1%
12858 5
< 0.1%
12857 2
 
< 0.1%
12856 3
< 0.1%
12855 3
< 0.1%

PERIMETER
Real number (ℝ)

HIGH CORRELATION 

Distinct45981
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean356.39262
Minimum261.04
Maximum523.891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size950.4 KiB
2023-10-26T09:26:05.635432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum261.04
5-th percentile289.547
Q1311.1765
median336.407
Q3411.21
95-th percentile456.6429
Maximum523.891
Range262.851
Interquartile range (IQR)100.0335

Descriptive statistics

Standard deviation56.651825
Coefficient of variation (CV)0.15895903
Kurtosis-0.96756986
Mean356.39262
Median Absolute Deviation (MAD)32.983
Skewness0.63166412
Sum21678294
Variance3209.4293
MonotonicityNot monotonic
2023-10-26T09:26:05.775458image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
297.785 8
 
< 0.1%
301.209 8
 
< 0.1%
300.208 8
 
< 0.1%
302.664 8
 
< 0.1%
299.24 7
 
< 0.1%
292.31 7
 
< 0.1%
296.503 7
 
< 0.1%
293.832 7
 
< 0.1%
307.254 6
 
< 0.1%
309.9 6
 
< 0.1%
Other values (45971) 60755
99.9%
ValueCountFrequency (%)
261.04 1
< 0.1%
262.372 1
< 0.1%
263.267 1
< 0.1%
264.531 1
< 0.1%
264.556 1
< 0.1%
264.738 1
< 0.1%
264.869 1
< 0.1%
265.424 1
< 0.1%
265.441 1
< 0.1%
266.242 1
< 0.1%
ValueCountFrequency (%)
523.891 1
< 0.1%
515.519 1
< 0.1%
512.011 1
< 0.1%
509.589 1
< 0.1%
509.458 1
< 0.1%
508.48 1
< 0.1%
508.096 1
< 0.1%
507.794 1
< 0.1%
506.675 1
< 0.1%
506.387 1
< 0.1%

MAJOR_AXIS
Real number (ℝ)

HIGH CORRELATION 

Distinct58519
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.48141
Minimum96.9683
Maximum247.6692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size950.4 KiB
2023-10-26T09:26:05.907982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum96.9683
5-th percentile110.38851
Q1124.8886
median143.3373
Q3187.0018
95-th percentile215.0462
Maximum247.6692
Range150.7009
Interquartile range (IQR)62.1132

Descriptive statistics

Standard deviation34.719714
Coefficient of variation (CV)0.22621446
Kurtosis-0.96328964
Mean153.48141
Median Absolute Deviation (MAD)25.2605
Skewness0.55843443
Sum9335813.7
Variance1205.4585
MonotonicityNot monotonic
2023-10-26T09:26:06.403071image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145.7455 4
 
< 0.1%
142.4949 4
 
< 0.1%
142.2209 3
 
< 0.1%
141.8145 3
 
< 0.1%
113.3817 3
 
< 0.1%
114.9189 3
 
< 0.1%
150.145 3
 
< 0.1%
138.914 3
 
< 0.1%
113.9787 3
 
< 0.1%
134.9148 3
 
< 0.1%
Other values (58509) 60795
99.9%
ValueCountFrequency (%)
96.9683 1
< 0.1%
98.3755 1
< 0.1%
98.6339 1
< 0.1%
98.9825 1
< 0.1%
99.1016 1
< 0.1%
99.4957 1
< 0.1%
99.5814 1
< 0.1%
99.6605 1
< 0.1%
99.6699 1
< 0.1%
99.7111 1
< 0.1%
ValueCountFrequency (%)
247.6692 1
< 0.1%
245.8789 1
< 0.1%
244.8736 1
< 0.1%
244.2602 1
< 0.1%
243.8224 1
< 0.1%
243.2096 1
< 0.1%
243.0976 1
< 0.1%
242.6468 1
< 0.1%
241.9453 1
< 0.1%
241.6255 1
< 0.1%

MINOR_AXIS
Real number (ℝ)

HIGH CORRELATION 

Distinct54841
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.021146
Minimum36.3274
Maximum100.2168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size950.4 KiB
2023-10-26T09:26:06.539097image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum36.3274
5-th percentile44.63716
Q148.861
median66.0172
Q372.632
95-th percentile79.07978
Maximum100.2168
Range63.8894
Interquartile range (IQR)23.771

Descriptive statistics

Standard deviation12.64228
Coefficient of variation (CV)0.20383821
Kurtosis-1.3669915
Mean62.021146
Median Absolute Deviation (MAD)10.3724
Skewness-0.041418923
Sum3772560.2
Variance159.82723
MonotonicityNot monotonic
2023-10-26T09:26:06.674622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.3638 5
 
< 0.1%
72.5395 5
 
< 0.1%
70.9164 4
 
< 0.1%
70.9436 4
 
< 0.1%
47.1964 4
 
< 0.1%
69.7109 4
 
< 0.1%
47.6609 4
 
< 0.1%
48.0686 4
 
< 0.1%
47.8165 4
 
< 0.1%
74.3875 4
 
< 0.1%
Other values (54831) 60785
99.9%
ValueCountFrequency (%)
36.3274 1
< 0.1%
36.5862 1
< 0.1%
36.6889 1
< 0.1%
36.8632 1
< 0.1%
36.9086 1
< 0.1%
37.0841 1
< 0.1%
37.2565 1
< 0.1%
37.5486 1
< 0.1%
37.5786 1
< 0.1%
37.6787 1
< 0.1%
ValueCountFrequency (%)
100.2168 1
< 0.1%
97.2941 1
< 0.1%
96.5326 1
< 0.1%
95.5433 1
< 0.1%
95.5422 1
< 0.1%
95.5049 1
< 0.1%
95.3369 1
< 0.1%
95.0969 1
< 0.1%
94.9568 1
< 0.1%
94.7853 1
< 0.1%

ECCENTRICITY
Real number (ℝ)

HIGH CORRELATION 

Distinct2879
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88485999
Minimum0.6891
Maximum0.9817
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size950.4 KiB
2023-10-26T09:26:06.804646image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.6891
5-th percentile0.7489
Q10.827
median0.8881
Q30.9547
95-th percentile0.9748
Maximum0.9817
Range0.2926
Interquartile range (IQR)0.1277

Descriptive statistics

Standard deviation0.0776351
Coefficient of variation (CV)0.087737157
Kurtosis-1.0539845
Mean0.88485999
Median Absolute Deviation (MAD)0.0656
Skewness-0.47561938
Sum53823.378
Variance0.0060272088
MonotonicityNot monotonic
2023-10-26T09:26:06.935169image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9727 137
 
0.2%
0.9726 130
 
0.2%
0.969 120
 
0.2%
0.9735 117
 
0.2%
0.9725 116
 
0.2%
0.9722 115
 
0.2%
0.9718 112
 
0.2%
0.9711 112
 
0.2%
0.9731 112
 
0.2%
0.9734 110
 
0.2%
Other values (2869) 59646
98.1%
ValueCountFrequency (%)
0.6891 1
 
< 0.1%
0.6893 1
 
< 0.1%
0.6894 1
 
< 0.1%
0.6898 2
< 0.1%
0.6901 3
< 0.1%
0.6902 1
 
< 0.1%
0.6909 1
 
< 0.1%
0.691 1
 
< 0.1%
0.6911 2
< 0.1%
0.6914 1
 
< 0.1%
ValueCountFrequency (%)
0.9817 9
< 0.1%
0.9816 11
< 0.1%
0.9815 11
< 0.1%
0.9814 5
 
< 0.1%
0.9813 13
< 0.1%
0.9812 10
< 0.1%
0.9811 12
< 0.1%
0.981 11
< 0.1%
0.9809 10
< 0.1%
0.9808 13
< 0.1%

EQDIASQ
Real number (ℝ)

HIGH CORRELATION 

Distinct6929
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.197653
Minimum70.7288
Maximum127.9803
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size950.4 KiB
2023-10-26T09:26:07.064693image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum70.7288
5-th percentile80.9214
Q187.8543
median94.3125
Q3100.6537
95-th percentile116.3709
Maximum127.9803
Range57.2515
Interquartile range (IQR)12.7994

Descriptive statistics

Standard deviation10.336079
Coefficient of variation (CV)0.10857493
Kurtosis0.92120725
Mean95.197653
Median Absolute Deviation (MAD)6.393
Skewness0.88414899
Sum5790587.7
Variance106.83452
MonotonicityNot monotonic
2023-10-26T09:26:07.202218image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81.0236 38
 
0.1%
79.916 34
 
0.1%
81.0079 31
 
0.1%
98.5832 31
 
0.1%
81.0315 31
 
0.1%
80.5903 30
 
< 0.1%
80.2261 30
 
< 0.1%
81.1178 30
 
< 0.1%
93.3491 30
 
< 0.1%
80.9214 30
 
< 0.1%
Other values (6919) 60512
99.5%
ValueCountFrequency (%)
70.7288 1
< 0.1%
71.6232 1
< 0.1%
73.7083 1
< 0.1%
74.3276 1
< 0.1%
75.0945 1
< 0.1%
76.3889 1
< 0.1%
76.6634 1
< 0.1%
77.0775 1
< 0.1%
77.4154 1
< 0.1%
78.209 1
< 0.1%
ValueCountFrequency (%)
127.9803 2
 
< 0.1%
127.9753 2
 
< 0.1%
127.9703 1
 
< 0.1%
127.9654 4
< 0.1%
127.9604 1
 
< 0.1%
127.9554 1
 
< 0.1%
127.9504 5
< 0.1%
127.9455 2
 
< 0.1%
127.9405 3
< 0.1%
127.9355 3
< 0.1%

SOLIDITY
Real number (ℝ)

HIGH CORRELATION 

Distinct378
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97619979
Minimum0.954
Maximum0.9921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size950.4 KiB
2023-10-26T09:26:07.340243image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.954
5-th percentile0.9635
Q10.971
median0.9764
Q30.9822
95-th percentile0.9871
Maximum0.9921
Range0.0381
Interquartile range (IQR)0.0112

Descriptive statistics

Standard deviation0.0073544074
Coefficient of variation (CV)0.0075337114
Kurtosis-0.56200317
Mean0.97619979
Median Absolute Deviation (MAD)0.0056
Skewness-0.29187068
Sum59379.304
Variance5.4087309 × 10-5
MonotonicityNot monotonic
2023-10-26T09:26:07.478769image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9752 337
 
0.6%
0.9754 333
 
0.5%
0.9782 324
 
0.5%
0.9737 318
 
0.5%
0.9743 318
 
0.5%
0.9764 311
 
0.5%
0.9769 309
 
0.5%
0.9777 309
 
0.5%
0.9736 308
 
0.5%
0.9772 307
 
0.5%
Other values (368) 57653
94.8%
ValueCountFrequency (%)
0.954 7
< 0.1%
0.9541 8
< 0.1%
0.9542 8
< 0.1%
0.9543 6
< 0.1%
0.9544 2
 
< 0.1%
0.9545 5
 
< 0.1%
0.9546 13
< 0.1%
0.9547 11
< 0.1%
0.9548 9
< 0.1%
0.9549 11
< 0.1%
ValueCountFrequency (%)
0.9921 1
 
< 0.1%
0.992 1
 
< 0.1%
0.9919 1
 
< 0.1%
0.9916 1
 
< 0.1%
0.9913 3
< 0.1%
0.9912 2
 
< 0.1%
0.9911 1
 
< 0.1%
0.991 2
 
< 0.1%
0.9909 5
< 0.1%
0.9908 3
< 0.1%

CONVEX_AREA
Real number (ℝ)

HIGH CORRELATION 

Distinct7181
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7377.8089
Minimum4032
Maximum13303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size950.4 KiB
2023-10-26T09:26:07.616293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum4032
5-th percentile5286
Q16186
median7141
Q38166
95-th percentile10889
Maximum13303
Range9271
Interquartile range (IQR)1980

Descriptive statistics

Standard deviation1666.3659
Coefficient of variation (CV)0.22586189
Kurtosis1.789981
Mean7377.8089
Median Absolute Deviation (MAD)990
Skewness1.218239
Sum4.4876998 × 108
Variance2776775.2
MonotonicityNot monotonic
2023-10-26T09:26:07.747318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5249 36
 
0.1%
6967 30
 
< 0.1%
5252 30
 
< 0.1%
6869 30
 
< 0.1%
5254 29
 
< 0.1%
5374 29
 
< 0.1%
5365 29
 
< 0.1%
6697 29
 
< 0.1%
7862 29
 
< 0.1%
6950 29
 
< 0.1%
Other values (7171) 60527
99.5%
ValueCountFrequency (%)
4032 1
< 0.1%
4136 1
< 0.1%
4363 1
< 0.1%
4444 1
< 0.1%
4545 1
< 0.1%
4661 1
< 0.1%
4708 1
< 0.1%
4758 1
< 0.1%
4844 1
< 0.1%
4941 1
< 0.1%
ValueCountFrequency (%)
13303 1
< 0.1%
13294 2
< 0.1%
13291 1
< 0.1%
13290 1
< 0.1%
13287 1
< 0.1%
13286 2
< 0.1%
13283 1
< 0.1%
13278 1
< 0.1%
13272 1
< 0.1%
13268 1
< 0.1%

EXTENT
Real number (ℝ)

HIGH CORRELATION 

Distinct5402
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63062191
Minimum0.3109
Maximum0.9017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size950.4 KiB
2023-10-26T09:26:07.868840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.3109
5-th percentile0.3829
Q10.532
median0.6627
Q30.7289
95-th percentile0.7953
Maximum0.9017
Range0.5908
Interquartile range (IQR)0.1969

Descriptive statistics

Standard deviation0.12858262
Coefficient of variation (CV)0.20389811
Kurtosis-0.65975167
Mean0.63062191
Median Absolute Deviation (MAD)0.0824
Skewness-0.60654797
Sum38358.839
Variance0.016533489
MonotonicityNot monotonic
2023-10-26T09:26:08.003364image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7106 43
 
0.1%
0.7138 40
 
0.1%
0.7002 38
 
0.1%
0.7063 37
 
0.1%
0.6889 37
 
0.1%
0.7264 37
 
0.1%
0.7059 37
 
0.1%
0.7155 37
 
0.1%
0.7259 36
 
0.1%
0.6862 36
 
0.1%
Other values (5392) 60449
99.4%
ValueCountFrequency (%)
0.3109 1
< 0.1%
0.3111 1
< 0.1%
0.3121 1
< 0.1%
0.3122 1
< 0.1%
0.3123 1
< 0.1%
0.3127 1
< 0.1%
0.3128 1
< 0.1%
0.3132 1
< 0.1%
0.3133 2
< 0.1%
0.3135 1
< 0.1%
ValueCountFrequency (%)
0.9017 1
< 0.1%
0.899 1
< 0.1%
0.8888 1
< 0.1%
0.8872 1
< 0.1%
0.8838 1
< 0.1%
0.8816 1
< 0.1%
0.8813 1
< 0.1%
0.8794 1
< 0.1%
0.8791 1
< 0.1%
0.8779 1
< 0.1%

ASPECT_RATIO
Real number (ℝ)

HIGH CORRELATION 

Distinct24592
Distinct (%)40.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6498776
Minimum1.3799
Maximum5.2572
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size950.4 KiB
2023-10-26T09:26:08.137889image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.3799
5-th percentile1.5089
Q11.77855
median2.1756
Q33.3613
95-th percentile4.48327
Maximum5.2572
Range3.8773
Interquartile range (IQR)1.58275

Descriptive statistics

Standard deviation1.0182856
Coefficient of variation (CV)0.38427645
Kurtosis-0.95605728
Mean2.6498776
Median Absolute Deviation (MAD)0.6435
Skewness0.59733873
Sum161184.1
Variance1.0369055
MonotonicityNot monotonic
2023-10-26T09:26:08.275915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.526 16
 
< 0.1%
1.4943 15
 
< 0.1%
1.9264 14
 
< 0.1%
1.5968 14
 
< 0.1%
1.5498 14
 
< 0.1%
1.5521 13
 
< 0.1%
1.5354 13
 
< 0.1%
1.9701 13
 
< 0.1%
1.5483 13
 
< 0.1%
1.6599 13
 
< 0.1%
Other values (24582) 60689
99.8%
ValueCountFrequency (%)
1.3799 1
< 0.1%
1.3803 1
< 0.1%
1.3805 1
< 0.1%
1.3811 1
< 0.1%
1.3812 1
< 0.1%
1.3817 2
< 0.1%
1.3818 1
< 0.1%
1.382 1
< 0.1%
1.3832 1
< 0.1%
1.3833 1
< 0.1%
ValueCountFrequency (%)
5.2572 1
< 0.1%
5.2524 1
< 0.1%
5.2519 1
< 0.1%
5.2513 2
< 0.1%
5.2509 1
< 0.1%
5.2504 1
< 0.1%
5.25 1
< 0.1%
5.2471 1
< 0.1%
5.2424 1
< 0.1%
5.2386 1
< 0.1%

ROUNDNESS
Real number (ℝ)

HIGH CORRELATION 

Distinct5112
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72913595
Minimum0.4214
Maximum0.9679
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size950.4 KiB
2023-10-26T09:26:08.409939image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.4214
5-th percentile0.4968
Q10.6059
median0.7757
Q30.8593
95-th percentile0.9244
Maximum0.9679
Range0.5465
Interquartile range (IQR)0.2534

Descriptive statistics

Standard deviation0.14761593
Coefficient of variation (CV)0.20245323
Kurtosis-1.3549373
Mean0.72913595
Median Absolute Deviation (MAD)0.1272
Skewness-0.22766103
Sum44351.153
Variance0.021790462
MonotonicityNot monotonic
2023-10-26T09:26:08.539463image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9129 46
 
0.1%
0.8211 39
 
0.1%
0.9075 39
 
0.1%
0.8171 38
 
0.1%
0.8205 38
 
0.1%
0.914 38
 
0.1%
0.92 38
 
0.1%
0.9006 37
 
0.1%
0.814 37
 
0.1%
0.9164 37
 
0.1%
Other values (5102) 60440
99.4%
ValueCountFrequency (%)
0.4214 1
< 0.1%
0.4247 1
< 0.1%
0.4276 1
< 0.1%
0.4287 1
< 0.1%
0.4294 1
< 0.1%
0.435 1
< 0.1%
0.4352 1
< 0.1%
0.4355 1
< 0.1%
0.4363 1
< 0.1%
0.4371 1
< 0.1%
ValueCountFrequency (%)
0.9679 1
< 0.1%
0.9669 1
< 0.1%
0.9659 1
< 0.1%
0.9645 1
< 0.1%
0.9644 1
< 0.1%
0.9636 1
< 0.1%
0.9624 1
< 0.1%
0.9623 1
< 0.1%
0.9622 1
< 0.1%
0.9614 1
< 0.1%

COMPACTNESS
Real number (ℝ)

HIGH CORRELATION 

Distinct4110
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64333224
Minimum0.4312
Maximum0.8492
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size950.4 KiB
2023-10-26T09:26:08.673987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.4312
5-th percentile0.4682
Q10.5403
median0.674
Q30.7456
95-th percentile0.8103
Maximum0.8492
Range0.418
Interquartile range (IQR)0.2053

Descriptive statistics

Standard deviation0.11832032
Coefficient of variation (CV)0.1839179
Kurtosis-1.4193176
Mean0.64333224
Median Absolute Deviation (MAD)0.1087
Skewness-0.081325836
Sum39131.97
Variance0.013999697
MonotonicityNot monotonic
2023-10-26T09:26:08.801011image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7061 49
 
0.1%
0.7872 46
 
0.1%
0.7081 46
 
0.1%
0.7116 45
 
0.1%
0.7105 43
 
0.1%
0.7913 43
 
0.1%
0.4831 42
 
0.1%
0.7841 41
 
0.1%
0.563 41
 
0.1%
0.7117 41
 
0.1%
Other values (4100) 60390
99.3%
ValueCountFrequency (%)
0.4312 1
< 0.1%
0.4316 1
< 0.1%
0.432 1
< 0.1%
0.4323 1
< 0.1%
0.4328 1
< 0.1%
0.433 1
< 0.1%
0.4331 2
< 0.1%
0.4333 1
< 0.1%
0.4334 1
< 0.1%
0.4337 1
< 0.1%
ValueCountFrequency (%)
0.8492 1
< 0.1%
0.8488 1
< 0.1%
0.848 1
< 0.1%
0.8479 2
< 0.1%
0.8476 2
< 0.1%
0.8475 2
< 0.1%
0.8474 1
< 0.1%
0.8472 1
< 0.1%
0.8471 1
< 0.1%
0.847 1
< 0.1%

SHAPEFACTOR_1
Real number (ℝ)

HIGH CORRELATION 

Distinct217
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.021771005
Minimum0.0131
Maximum0.0357
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size950.4 KiB
2023-10-26T09:26:08.929534image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0131
5-th percentile0.0163
Q10.0177
median0.0195
Q30.0266
95-th percentile0.029
Maximum0.0357
Range0.0226
Interquartile range (IQR)0.0089

Descriptive statistics

Standard deviation0.0047027895
Coefficient of variation (CV)0.2160116
Kurtosis-1.4055417
Mean0.021771005
Median Absolute Deviation (MAD)0.0028
Skewness0.36207503
Sum1324.2649
Variance2.2116229 × 10-5
MonotonicityNot monotonic
2023-10-26T09:26:09.064559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0179 1327
 
2.2%
0.0177 1321
 
2.2%
0.0176 1307
 
2.1%
0.018 1273
 
2.1%
0.0178 1273
 
2.1%
0.0175 1261
 
2.1%
0.0174 1247
 
2.1%
0.0181 1185
 
1.9%
0.0173 1164
 
1.9%
0.0182 1159
 
1.9%
Other values (207) 48310
79.4%
ValueCountFrequency (%)
0.0131 1
 
< 0.1%
0.0134 2
 
< 0.1%
0.0135 5
 
< 0.1%
0.0136 1
 
< 0.1%
0.0137 2
 
< 0.1%
0.0138 10
 
< 0.1%
0.0139 17
< 0.1%
0.014 18
< 0.1%
0.0141 24
< 0.1%
0.0142 38
0.1%
ValueCountFrequency (%)
0.0357 1
 
< 0.1%
0.0356 1
 
< 0.1%
0.0354 1
 
< 0.1%
0.0353 1
 
< 0.1%
0.0349 1
 
< 0.1%
0.0348 2
< 0.1%
0.0347 1
 
< 0.1%
0.0343 1
 
< 0.1%
0.0342 1
 
< 0.1%
0.0341 3
< 0.1%

SHAPEFACTOR_2
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0088236293
Minimum0.0053
Maximum0.0133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size950.4 KiB
2023-10-26T09:26:09.195082image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0053
5-th percentile0.006
Q10.0069
median0.009
Q30.0103
95-th percentile0.0116
Maximum0.0133
Range0.008
Interquartile range (IQR)0.0034

Descriptive statistics

Standard deviation0.0018422249
Coefficient of variation (CV)0.20878312
Kurtosis-1.1184125
Mean0.0088236293
Median Absolute Deviation (MAD)0.0017
Skewness-0.0526117
Sum536.7149
Variance3.3937926 × 10-6
MonotonicityNot monotonic
2023-10-26T09:26:09.334608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0091 2198
 
3.6%
0.009 2121
 
3.5%
0.0092 2073
 
3.4%
0.0089 1993
 
3.3%
0.0093 1964
 
3.2%
0.0088 1827
 
3.0%
0.0094 1582
 
2.6%
0.0087 1548
 
2.5%
0.0064 1500
 
2.5%
0.0065 1476
 
2.4%
Other values (71) 42545
69.9%
ValueCountFrequency (%)
0.0053 8
 
< 0.1%
0.0054 23
 
< 0.1%
0.0055 67
 
0.1%
0.0056 180
 
0.3%
0.0057 385
 
0.6%
0.0058 604
1.0%
0.0059 870
1.4%
0.006 1147
1.9%
0.0061 1271
2.1%
0.0062 1350
2.2%
ValueCountFrequency (%)
0.0133 1
 
< 0.1%
0.0132 1
 
< 0.1%
0.0131 2
 
< 0.1%
0.013 4
 
< 0.1%
0.0129 5
 
< 0.1%
0.0128 22
 
< 0.1%
0.0127 17
 
< 0.1%
0.0126 33
 
0.1%
0.0125 67
0.1%
0.0124 94
0.2%

SHAPEFACTOR_3
Real number (ℝ)

HIGH CORRELATION 

Distinct5197
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42787595
Minimum0.1859
Maximum0.7212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size950.4 KiB
2023-10-26T09:26:09.476134image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.1859
5-th percentile0.2192
Q10.2919
median0.4543
Q30.5559
95-th percentile0.6566
Maximum0.7212
Range0.5353
Interquartile range (IQR)0.264

Descriptive statistics

Standard deviation0.15147225
Coefficient of variation (CV)0.35400972
Kurtosis-1.4083397
Mean0.42787595
Median Absolute Deviation (MAD)0.1431
Skewness0.078041674
Sum26026.411
Variance0.022943841
MonotonicityNot monotonic
2023-10-26T09:26:09.615160image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2334 43
 
0.1%
0.2327 41
 
0.1%
0.3169 40
 
0.1%
0.2253 39
 
0.1%
0.3195 38
 
0.1%
0.2403 37
 
0.1%
0.228 37
 
0.1%
0.3122 37
 
0.1%
0.229 36
 
0.1%
0.3113 36
 
0.1%
Other values (5187) 60443
99.4%
ValueCountFrequency (%)
0.1859 1
< 0.1%
0.1863 1
< 0.1%
0.1866 1
< 0.1%
0.1869 1
< 0.1%
0.1873 1
< 0.1%
0.1874 1
< 0.1%
0.1875 2
< 0.1%
0.1878 1
< 0.1%
0.1879 1
< 0.1%
0.1881 1
< 0.1%
ValueCountFrequency (%)
0.7212 1
< 0.1%
0.7205 1
< 0.1%
0.7191 1
< 0.1%
0.7189 2
< 0.1%
0.7184 2
< 0.1%
0.7183 1
< 0.1%
0.7182 1
< 0.1%
0.718 1
< 0.1%
0.7177 1
< 0.1%
0.7175 2
< 0.1%

SHAPEFACTOR_4
Real number (ℝ)

HIGH CORRELATION 

Distinct311
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98592459
Minimum0.968
Maximum0.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size950.4 KiB
2023-10-26T09:26:09.746683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.968
5-th percentile0.9738
Q10.9816
median0.9865
Q30.991
95-th percentile0.9956
Maximum0.999
Range0.031
Interquartile range (IQR)0.0094

Descriptive statistics

Standard deviation0.0065803712
Coefficient of variation (CV)0.006674315
Kurtosis-0.44539808
Mean0.98592459
Median Absolute Deviation (MAD)0.0047
Skewness-0.40861811
Sum59970.835
Variance4.3301285 × 10-5
MonotonicityNot monotonic
2023-10-26T09:26:09.883709image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9874 384
 
0.6%
0.9887 374
 
0.6%
0.9859 370
 
0.6%
0.9873 369
 
0.6%
0.9865 368
 
0.6%
0.9849 365
 
0.6%
0.9886 365
 
0.6%
0.9881 361
 
0.6%
0.9864 360
 
0.6%
0.9888 359
 
0.6%
Other values (301) 57152
94.0%
ValueCountFrequency (%)
0.968 31
0.1%
0.9681 31
0.1%
0.9682 23
< 0.1%
0.9683 34
0.1%
0.9684 30
< 0.1%
0.9685 32
0.1%
0.9686 36
0.1%
0.9687 39
0.1%
0.9688 33
0.1%
0.9689 31
0.1%
ValueCountFrequency (%)
0.999 2
 
< 0.1%
0.9989 3
 
< 0.1%
0.9988 6
 
< 0.1%
0.9987 12
 
< 0.1%
0.9986 10
 
< 0.1%
0.9985 12
 
< 0.1%
0.9984 9
 
< 0.1%
0.9983 17
< 0.1%
0.9982 22
< 0.1%
0.9981 31
0.1%

CLASS
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size950.4 KiB
Arborio
14930 
Karacadag
14859 
Jasmine
14350 
Basmati
13696 
Ipsala
2992 

Length

Max length9
Median length7
Mean length7.4393773
Min length6

Characters and Unicode

Total characters452515
Distinct characters20
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBasmati
2nd rowArborio
3rd rowJasmine
4th rowBasmati
5th rowArborio

Common Values

ValueCountFrequency (%)
Arborio 14930
24.5%
Karacadag 14859
24.4%
Jasmine 14350
23.6%
Basmati 13696
22.5%
Ipsala 2992
 
4.9%

Length

2023-10-26T09:26:10.011732image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-26T09:26:10.124253image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
arborio 14930
24.5%
karacadag 14859
24.4%
jasmine 14350
23.6%
basmati 13696
22.5%
ipsala 2992
 
4.9%

Most occurring characters

ValueCountFrequency (%)
a 107162
23.7%
r 44719
9.9%
i 42976
 
9.5%
s 31038
 
6.9%
o 29860
 
6.6%
m 28046
 
6.2%
A 14930
 
3.3%
b 14930
 
3.3%
K 14859
 
3.3%
c 14859
 
3.3%
Other values (10) 109136
24.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 391688
86.6%
Uppercase Letter 60827
 
13.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 107162
27.4%
r 44719
11.4%
i 42976
11.0%
s 31038
 
7.9%
o 29860
 
7.6%
m 28046
 
7.2%
b 14930
 
3.8%
c 14859
 
3.8%
d 14859
 
3.8%
g 14859
 
3.8%
Other values (5) 48380
12.4%
Uppercase Letter
ValueCountFrequency (%)
A 14930
24.5%
K 14859
24.4%
J 14350
23.6%
B 13696
22.5%
I 2992
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 452515
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 107162
23.7%
r 44719
9.9%
i 42976
 
9.5%
s 31038
 
6.9%
o 29860
 
6.6%
m 28046
 
6.2%
A 14930
 
3.3%
b 14930
 
3.3%
K 14859
 
3.3%
c 14859
 
3.3%
Other values (10) 109136
24.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 452515
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 107162
23.7%
r 44719
9.9%
i 42976
 
9.5%
s 31038
 
6.9%
o 29860
 
6.6%
m 28046
 
6.2%
A 14930
 
3.3%
b 14930
 
3.3%
K 14859
 
3.3%
c 14859
 
3.3%
Other values (10) 109136
24.1%

Interactions

2023-10-26T09:26:03.140972image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:38.747007image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:40.306292image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:42.009104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:43.603897image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:45.189186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:46.940506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:48.524295image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:50.168597image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:51.922918image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:53.480704image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:55.075495image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:56.612777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:58.388601image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:59.925383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:26:01.592189image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:26:03.235990image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:38.837525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:40.397309image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:42.103622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:43.693913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:45.279703image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:47.030523image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:48.622814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2023-10-26T09:25:52.013935image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2023-10-26T09:26:02.840416image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:26:04.556731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:40.111756image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:41.815067image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:43.400358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:44.991651image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:46.743971image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:48.319259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:49.965559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:51.730383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:53.283667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:54.873458image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:56.419240image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:58.202067image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:59.732347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:26:01.381649image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:26:02.946435image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:26:04.654749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:40.206774image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:41.908586image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:43.501376image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:45.087668image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:46.839488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:48.416276image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:50.065078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:51.824900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:53.379685image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:54.971976image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:56.513758image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:58.291584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:25:59.826365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:26:01.484669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-26T09:26:03.041454image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-10-26T09:26:10.226771image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
AREAPERIMETERMAJOR_AXISMINOR_AXISECCENTRICITYEQDIASQSOLIDITYCONVEX_AREAEXTENTASPECT_RATIOROUNDNESSCOMPACTNESSSHAPEFACTOR_1SHAPEFACTOR_2SHAPEFACTOR_3SHAPEFACTOR_4CLASS
AREA1.0000.7390.4830.4400.1311.000-0.0430.999-0.0190.131-0.143-0.132-0.434-0.493-0.132-0.0110.669
PERIMETER0.7391.0000.931-0.2170.7380.739-0.4490.751-0.4200.738-0.752-0.7400.225-0.933-0.740-0.3890.609
MAJOR_AXIS0.4830.9311.000-0.5130.9140.483-0.5130.497-0.5280.914-0.914-0.9150.519-0.999-0.915-0.4720.691
MINOR_AXIS0.440-0.217-0.5131.000-0.8020.4400.4430.4260.508-0.8020.7820.801-0.9990.5050.8010.4130.638
ECCENTRICITY0.1310.7380.914-0.8021.0000.131-0.5550.148-0.6001.000-0.989-1.0000.805-0.910-1.000-0.5070.812
EQDIASQ1.0000.7390.4830.4400.1311.000-0.0430.999-0.0190.131-0.143-0.132-0.434-0.493-0.132-0.0110.663
SOLIDITY-0.043-0.449-0.5130.443-0.555-0.0431.000-0.0770.419-0.5550.6350.565-0.4650.4950.5650.7280.343
CONVEX_AREA0.9990.7510.4970.4260.1480.999-0.0771.000-0.0320.148-0.163-0.148-0.419-0.506-0.148-0.0340.668
EXTENT-0.019-0.420-0.5280.508-0.600-0.0190.419-0.0321.000-0.6000.6150.601-0.5130.5230.6010.3700.485
ASPECT_RATIO0.1310.7380.914-0.8021.0000.131-0.5550.148-0.6001.000-0.989-1.0000.805-0.910-1.000-0.5070.836
ROUNDNESS-0.143-0.752-0.9140.782-0.989-0.1430.635-0.1630.615-0.9891.0000.991-0.7890.9070.9910.5830.841
COMPACTNESS-0.132-0.740-0.9150.801-1.000-0.1320.565-0.1480.601-1.0000.9911.000-0.8050.9101.0000.5220.831
SHAPEFACTOR_1-0.4340.2250.519-0.9990.805-0.434-0.465-0.419-0.5130.805-0.789-0.8051.000-0.510-0.805-0.4460.609
SHAPEFACTOR_2-0.493-0.933-0.9990.505-0.910-0.4930.495-0.5060.523-0.9100.9070.910-0.5101.0000.9100.4420.681
SHAPEFACTOR_3-0.132-0.740-0.9150.801-1.000-0.1320.565-0.1480.601-1.0000.9911.000-0.8050.9101.0000.5220.829
SHAPEFACTOR_4-0.011-0.389-0.4720.413-0.507-0.0110.728-0.0340.370-0.5070.5830.522-0.4460.4420.5221.0000.309
CLASS0.6690.6090.6910.6380.8120.6630.3430.6680.4850.8360.8410.8310.6090.6810.8290.3091.000

Missing values

2023-10-26T09:26:04.802776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-26T09:26:05.064324image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AREAPERIMETERMAJOR_AXISMINOR_AXISECCENTRICITYEQDIASQSOLIDITYCONVEX_AREAEXTENTASPECT_RATIOROUNDNESSCOMPACTNESSSHAPEFACTOR_1SHAPEFACTOR_2SHAPEFACTOR_3SHAPEFACTOR_4CLASS
07805437.915209.821548.02210.973599.68770.977579850.35474.36930.51140.47510.02690.00620.22570.9863Basmati
17503340.757138.336169.84170.863297.74000.966077670.66371.98070.81200.70650.01840.00930.49920.9888Arborio
25124314.617141.980346.57840.944780.77180.972152710.47603.04820.65050.56890.02770.00910.32360.9865Jasmine
37990437.085201.438651.22450.9671100.86220.965982720.62743.93250.52560.50070.02520.00640.25070.9859Basmati
47433342.893140.335068.39270.873297.28300.983175610.60062.05190.79440.69320.01890.00920.48060.9860Arborio
511648445.527178.465984.93270.8795121.78130.9599121350.56602.10130.73740.68240.01530.00730.46560.9784Ipsala
67621450.325219.098145.23010.978598.50560.971878420.33984.84410.47220.44960.02870.00590.20210.9792Basmati
78582367.338146.612875.54060.8570104.53200.974088110.64231.94080.79920.71300.01710.00880.50830.9866Arborio
85450320.362139.996350.69100.932183.30160.962656620.55022.76180.66730.59500.02570.00930.35410.9778Jasmine
96781307.023116.244374.80930.765492.91840.981969060.72081.55390.90400.79930.01710.01100.63890.9928Karacadag
AREAPERIMETERMAJOR_AXISMINOR_AXISECCENTRICITYEQDIASQSOLIDITYCONVEX_AREAEXTENTASPECT_RATIOROUNDNESSCOMPACTNESSSHAPEFACTOR_1SHAPEFACTOR_2SHAPEFACTOR_3SHAPEFACTOR_4CLASS
749855623284.024112.495964.27710.820784.61340.976657580.67751.75020.87590.75210.02000.01140.56570.9901Karacadag
749885952373.822177.441243.09890.970187.05360.977360900.42394.11710.53520.49060.02980.00720.24070.9910Basmati
749895820281.088103.554772.50510.714086.08280.976359610.70631.42820.92570.83130.01780.01250.69100.9869Karacadag
749915110315.532140.944647.41660.941780.66140.967352830.56932.97250.64500.57230.02760.00930.32750.9735Jasmine
749927076417.090198.148146.23530.972494.91810.968373080.46594.28560.51110.47900.02800.00650.22950.9834Basmati
749937812416.943196.355351.50250.965099.73240.982979480.39023.81250.56470.50790.02510.00660.25800.9836Basmati
749946230289.151108.793773.55880.736889.06340.986963130.77331.47900.93640.81860.01750.01180.67020.9912Karacadag
749955551285.911114.169562.90790.834584.06990.984656380.64181.81490.85330.73640.02060.01130.54220.9841Arborio
749967696322.703121.390081.13750.743898.98920.986877990.73091.49610.92870.81550.01580.01050.66500.9949Karacadag
749977579339.295136.312571.28660.852498.23380.980577300.63991.91220.82730.72070.01800.00940.51930.9931Arborio

Duplicate rows

Most frequently occurring

AREAPERIMETERMAJOR_AXISMINOR_AXISECCENTRICITYEQDIASQSOLIDITYCONVEX_AREAEXTENTASPECT_RATIOROUNDNESSCOMPACTNESSSHAPEFACTOR_1SHAPEFACTOR_2SHAPEFACTOR_3SHAPEFACTOR_4CLASS# duplicates
08857374.838153.794174.68320.8742106.19360.973790960.66342.05930.79220.69050.01740.00840.47680.9818Ipsala2
19089389.317165.514770.83310.9038107.57540.971793540.79662.33670.75360.64990.01820.00780.42240.9871Ipsala2
29102383.080160.904573.14430.8907107.65230.980692820.57362.19980.77940.66900.01770.00800.44760.9847Ipsala2
39181381.423158.421574.41120.8828108.11850.975794100.60292.12900.79300.68250.01730.00810.46580.9916Ipsala2
49235397.780173.675067.98830.9202108.43600.976694560.61402.55450.73340.62440.01880.00740.38980.9958Ipsala2
59462409.356173.154670.74000.9127109.76060.960798490.71442.44780.70960.63390.01830.00750.40180.9835Ipsala2
69519392.404157.602878.90530.8656110.09070.965198630.63121.99740.77680.69850.01660.00830.48790.9746Ipsala2
79714401.359166.894975.57080.8916111.21260.9699100150.76542.20850.75780.66640.01720.00780.44400.9806Ipsala2
89761406.743165.783476.84870.8861111.48130.9609101580.70862.15730.74140.67250.01700.00790.45220.9755Ipsala2
99800408.173171.609274.39000.9012111.70380.9665101400.75312.30690.73920.65090.01750.00760.42370.9774Ipsala2